A novel PNN model with training algorithms is proposed for class conditional density estimation.
提出了一种新的类条件密度函数估计的PNN模型及其算法。
The conditional density and the conditional mean are estimated by nonparametric method. Then the estimator of the parameter is proposed by the generalized method of moments.
使用非参数估计方法给出条件密度和条件均值的估计，在此基础上给出参数的广义矩估计。
We propose a method for proving two matrix identities, which are often used in statistical analysis, based on knowledge of conditional posterior density.
提出了用求条件后验密度的方法证明统计分析中的两个矩阵等式的方法。
This thesis is composed of two sections in which we discuss generalized spectral density test of conditional autoregressive heteroscedasticity for threshold autoregressive model.
本文分两节对门限自回归模型中自回归条件异方差的广义谱密度检验进行了讨论。在第一节中，我们介绍了广义谱密度检验。
When the state process has Markov property, the recursive formulae for the conditional distribution density functions of filtering, prediction. and interpolation, are given respectively.
当状态过程具有马氏性时，还分别给出滤波、预测以及内插的条件分布密度函数的递推格式。
A discrete method for stochastic variable (features) space of class-conditional-probability density and estimation method for class-conditional -probability distribution is proposed.
本文提出了类条件概率密度随机变量（特征）空间离散化及类条件概率分布估计方法。
A discrete method for stochastic variable (features) space of class-conditional-probability density and estimation method for class-conditional -probability distribution is proposed.
本文提出了类条件概率密度随机变量（特征）空间离散化及类条件概率分布估计方法。
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